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Tutorial AVL TREES 1 Tutorial AVL TREES Binary search trees are designed for efficient access to data. In some cases, however, a binary search tree is degenerate or "almost degenerate" with most of the n elements descending as a linked list in one of the subtrees of a node. The search efficiency of the tree becomes O(n). A complete binary tree is the other extreme. Nodes are uniformly distributed among left and right subtrees allowing the structure to store n elements in a tree of minimum height. The longest path is log2 n +1 and the search efficiency is O(log2 n). Intuitively, a degenerate binary tree is very "unbalanced" in the sense that all of the nodes lie in one of the subtrees of the root. On the other hand, a complete binary tree is "balanced" in the sense that nodes are equitably distributed between the two subtrees of a node. Ideally, all binary search trees would be complete trees. This is not possible with random data. The problem is the insert() and erase() algorithms that add or remove an element without any follow-up analysis to determine how the action affects the overall balance of the tree. To address this problem, researchers Adelson, Velskii and Landis defined the concept of height-balance for a node and developed new search tree insert and erase algorithms that would reorder the elements to maintain height-balance. The binary search trees with these new algorithms are called AVL trees after their creators. Besides the usual search-ordering of nodes it the tree, an AVL tree is height- balanced. By this we mean that for each node in the tree, the difference in height (depth) of its two subtrees is at most 1. Figure 1 displays equivalent binary search and AVL trees that store array data. The first example uses array arrA, whose elements are in ascending order while the second example uses array arrB, whose elements are in random order. arrA[5] = {1,2,3,4,5} arrB[8] = {20,30,80,40,10,60,50,70} Binary Search Tree AVL Tree 1 2 2 1 4 5 3 3 4 arrA = {1, 2, 3, 4, 5} 5 Binary Search Tree AVL Tree 20 30 10 30 20 80 80 10 40 80 40 60 50 70 50 70 arrB = {20,30,80,40,10,60,50,70} FIGURE 1 Equivalent Binary Search and AVL Trees 2 The binary search tree for array arrA has a height of 5, whereas the AVL tree has a height of 2. In general, the height of an AVL tree never exceeds O(log2 n). This fact makes an AVL tree an efficient search container when rapid access to elements is demanded. 1 AVL Tree Nodes AVL trees are modeled after binary search trees. The operations are identical although the action of the AVL tree insert() and erase() functions are quite different since they must preserve the balance feature of the tree. To maintain a measure of balance, we define an avlTreeNode object with the integer balanceFactor as an additional field. left nodeValue balanceFactor right avlTreeNode The value of the field is the difference between the height of the right and left subtrees of the node. balanceFactor = height(right-subtree) - height(left-subtree) If balanceFactor is negative, the node is "heavy on the left" since the height of the left subtree is greater than the height of the right subtree. With balanceFactor positive, the node is "heavy on the right." A balanced node has balanceFactor = 0. An AVL tree is a binary search tree in which the balance-factor of each node is in the range -1 to 1. Figure 2 describes three AVL trees with tags -1, 0, or +1 on each node to indicate its balance-factor (relative height of the left and right subtrees). -1: height of the left subtree is one greater than the right subtree. 0: height of the left and right subtrees are equal. +1: height of the right subtree is one greater than the left subtree. (a) 10 (b) 50 (c) 50 (+1) (-1) (+1) 20 20 60 20 60 (0) (+1) (0) (0) (+1) 40 80 (0) (0) FIGURE 2 AVL trees with balance-factor of each node in parentheses The avlTreeNode class is similar to the tnode class for binary search trees. Four public data members include the data value, the left and right pointers, and the balance factor. The constructor is used to create a node for an AVL tree and initialize the data members. 3 DECLARATION: avlTreeNode CLASS // declares a tree node object for a binary tree template <typename T> class avlTreeNode { public: // data in the node T nodeValue; // pointers to the left and right children of the node avlTreeNode<T> *left; avlTreeNode<T> *right; int balanceFactor; // CONSTRUCTOR avlTreeNode (const T& item, avlTreeNode<T> *lptr = NULL, avlTreeNode<T> *rptr = NULL, int bal = 0): nodeValue(item), left(lptr), right(rptr), balanceFactor(bal) {} }; 2 The avlTree Class The avlTree class with the same programmer-interface used by the stree. The class has both constant and non- constant iterators, which can be used to scan the list of elements. A non-constant version is supplied so that a program can use the deference operator * to update the data value of a node. This feature can be used when the AVL trees stores records with a key and other data values and the update modifies only the data values. An attempt to update the key could destroy the structure of the tree. Like BinSTree iterators, AVL iterators are forward iterators but with an important limitation. Since a tree may need to be rebalanced when an item is added or removed , the insert() and erase() operations invalidate all iterators. To be used again, the iterators must be reset to the start of the list with begin(). The following is a declaration of the programmer-interface for the avlTree class. We implement only the insert() method and do not discuss the erase() methods. The method clear() is added to remove all of the nodes in the tree. In the stree class, clear is executed by calling erase() with the iterator range [begin(), end()). The memory management functions are not included We will expand the declaration to include the private member functions when we discuss the implementation of the class. DECLARATION: avlTree CLASS (Programmer-Interface template <typename T> class avlTree { // CONSTRUCTORS, DESTRUCTOR, ASSIGNMENT // constructor. initialize root to NULL and size to 0 avlTree(); // constructor. insert n elements from range of T* pointers 4 avlTree(T *first, T *last); // search for item. if found, return an iterator pointing // at it in the tree; otherwise, return end() iterator find(const T& item); // search for item. if found, return an iterator pointing // at it in the tree; otherwise, return end() const_iterator find(const T& item) const; // indicate whether the tree is empty int empty() const; // return the number of data items in the tree int size() const; // give a vertical display of the tree . void displayTree(int maxCharacters) const; // insert item into the tree //pair<iterator, bool> insert(const T& item); // insert a new node using the basic List operation and format pair<iterator, bool> insert(const T& item); // delete all the nodes in the tree void clear(); // constant versions iterator begin(); iterator end(); const_iterator begin() const; const_iterator end() const; }; The function displayTree() is similar to displayTree() in the stree class. It includes both the label and the balance-factor for each node in the format <label> : <balanceFactor> EXAMPLE 1 The example illustrates the use of the avlTree operations. 1. Declare an avlTree object avltreeA that stores integers and an object avlTreeB that stores real numbers with initial values from array arrB. // avlTreeA is empty tree of integers avlTree<int> avlTreeA; // avlTreeB is a tree of reals with 6 initial values double arrB[6] = {2.8, 3.9, -2.0, 4.9, 8.6, -12.8}; avlTree<double> avltreeB(arrB, arrB+6); 2. A loop initializes avlTreeA with integers 0 to 9. The tree is displayed with displayTree() for (i = 1; i < 10; i++) 5 avlTreeA.insert(i); avlTreeA.displayTree(2); 3:1 1:0 7:0 0:0 2:0 5:0 8:1 4:0 6:0 9:0 3. Determine whether 8.6 and 1.6 are elements in avlTreeB. avlTree<double>::iterator dblIter; if ((dblIter = avlTreeB.find(8.6)) != avlTreeB.end()) cout << "Element " << *dblIter << " is an element in AVL tree" << endl; if ((dblIter = avlTreeB.find(1.6)) == avlTreeB.end()) cout << "Element 1.6 is not an element in AVL tree" << endl; Solution: Element 8.6 is an element in AVL tree Element 1.6 is not an element in AVL tree 3 Application: Updating Character Counts An AVL tree is most effective in situations where the data statically resides in the tree and the application primarily searches for items and updates their value. A simple example declares objects of type charCount that contain a character and its frequency (count) as data members. The class has a constructor that with a char argument that initializes the data member and sets the count to 1. The class also provides overloaded versions of the comparison operators < and == along with overloaded version of the << operator. The comparison operators allow charCount to be used as the template type for avlTree objects and their implementation compares only at the char value of the operands. The << operator outputs the character and its count in the form <char>(<count>). In the example, we count the number of occurrences of the letters 'a' to 'z' in the 25000+ word dictionary "words.dat".
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